Le Zhang, P.N. Suganthan
IEEE Computational Intelligence Magazine, vol. 12, no. 4, pp. 61-72, Nov. 2017, doi: 10.1109/MCI.2017.2742867
Publication year: 2017

Studies in machine learning have shown promising classification performance of ensemble methods employing “perturb and combine” strategies. In particular, the classical random forest algorithm performs the best among 179 classifiers on 121 UCI datasets from different domains. Motivated by this observation, we extend our previous work on oblique decision tree ensemble. We also propose an efficient co-trained kernel ridge regression method. In addition, a random vector functional link network ensemble is also introduced. Our experiments show that our two oblique decision tree ensemble variants and the co-trained kernel ridge regression ensemble are the top three ranked methods among the 183 classifiers. The proposed random vector functional link network ensemble also outperforms all neural network based methods used in the experiments.


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